Who’s HiPeRT Lab?

The High-Performance Real-Time Laboratory (HiPeRT Lab) has been founded in 2012 at the University of Modena. It involves more than 40 researchers developing algorithmic and software solutions for high-performance real-time system. HiPeRT Lab’s mission is to predictably exploit the tremendous performance offered by next-generation embedded platforms in application domains where timing and safety requirements are crucial. To this end, the group acquired a strong expertise throughout the whole technological stack, from low-level hardware profiling and characterization, to Real-Time Operating Systems and Hypervisors, to predictable compilers and parallel programming models. The lab hosts multiple working prototypes, including autonomous vehicles (multiple cars, delivery bots, RC models, drones, etc.) and industrial automation systems. HiPeRT Lab is involved in several EU and industrial projects in automotive, avionics and industrial automation domains. It strongly believes in the technology transfer between academia and industry, fostering and promoting new collaborations for improving and devising the real-time systems of the future.

SELF-DRIVING CARS
AND AUTONOMOUS VEHICLES

The golden era of embedded super-computers entered a phase where hardware platforms are mature enough to power real production systems.
Especially, the dream of having self-driving autonomous vehicles is
quickly shaping, and both industries and academics are investing
resources in this direction. HiPeRT joins the effort with a framework of
technologies and software components aggregated in several L3/4
prototypes with full/semi-autonomous capabilities.
These efforts are supported both by industrial and public projects, and
cover multiple application domains: from “standard” to racing cars, from last-mile delivery
bots, to laser-guided vehicles for warehouse management, to autonomous
swarms of aerial and marine drones.

SELF-DRIVING CARS AND ATUONOMOUS VEHICLES

HiPeRT joins the effort with a framework of technologies and software components aggregated in several L3/4 prototypes with full/semi-autonomous capabilities. These efforts are supported both by industrial and public projects, and cover multiple application domains: from “standard” to racing cars, from last-mile delivery bots, to laser-guided vehicles for warehouse management, to autonomous swarms of aerial and marine drones.

REAL-TIME EMBEDDED SYSTEM

The mission of the Systems team in HiPeRT starts from hardware characterization
and profiling and instruments hypervisors with novel software techniques that
provide a real-time-enabling layer where multi-OS/RTOS solutions can be safely
hosted guaranteeing the crucial Freedom-from-Interference property. Custom
compiler optimisation and task profiling are keys to either control or know,
respectively, the application's behaviour. This enables revolutionary
memory-centric platform scheduling and predictable execution models on the one
hand. On the other hand, rich, DAG-like task models are allocated on the
architecture heterogeneity to minimise response time while avoiding any
deviation.

HiPeRT Lab is in the core organization team of the F1/10 autonomous racing competition, which takes place twice per year and gathers a world-wide community of researchers, engineers and simply tech lovers for Autonomous Driving Systems. We also won the New York edition in 2019.

The HiPeRT Lab, together with the city of Modena, Maserati and the Italian Ministry of Transportation,
deployed the Modena Automotive Smart Area MASA, a square kilometer area with a
pervasive coverage of smart sensors, network infrastructure and computing devices for real-time data
analytics.

In the field of Artificial Intelligence, the HiPeRT Lab focuses on Convolutional Neural Networks (CNN) for object detection and segmentation, neural network quantization, meta-learning for automatic hyper-parameter tuning and mathematical optimization to strengthen the robustness of the methods.

REAL-TIME EMBEDDED SYSTEMS

The mission of the Systems team in HiPeRT starts from hardware characterization
and profiling and instruments hypervisors with novel software techniques that
provide a real-time-enabling layer where multi-OS/RTOS solutions can be safely
hosted guaranteeing the crucial Freedom-from-Interference property. Custom
compiler optimisation and task profiling are keys to either control or know,
respectively, the application’s behaviour. This enables revolutionary
memory-centric platform scheduling and predictable execution models on the one
hand. On the other hand, rich, DAG-like task models are allocated on the
architecture heterogeneity to minimise response time while avoiding any
deviation.

PRYSTINE will realize Fail-operational Urban Surround perceptION (FUSION) which is based on robust Radar and LiDAR sensor fusion and control functions in order to enable safe automated driving in urban and rural environments.

The challenge of the I-MECH project is to bridge the gap between the latest research results and best industrial practice in intelligent motion control for smart mechatronic systems. Software and hardware building blocks, featuring standardized interfaces, will be developed to deliver a complete I-MECH reference motion control platform.

NewControl will deliver fail-operational holistic virtualized platforms for vehicular subsystems that are critical to automated driving (SAE Levels 3+), enabling mobility-as-a-service for the next generation of highly automated vehicles.

ECSEL (H2020), Grant Agreement: 826653

5G-CARMEN5G-Carmen aims to develop new solutions based on the 5G technology for enabling Connected & Automated Mobility in Cross-Borders/Corridors scenarios, where new technologies & architectures must be put in place to ensure continuous/smooth operations at National & European levels.

H2020, Grant Agreement: 825012

FRACTALThe objective of FRACTAL is to design a reliable computing node that will create a Cognitive Edge under industry standards, the building block of scalable Internet of Things , with the capability of learning how to improve its performance against the uncertainty of the environment.

ECSEL (H2020), Grant Agreement: TBD

InSecTTThe objectives of InSecTT are to develop solutions for Intelligent, Secure, Trustable Things applied in industrial solutions for European industry throughout the whole Supply Chain. This is a joint cooperation with AIMageLab of UNIMORE, coordinated by Consorzio Interuniversitario Nazionale per l’Informatica.

H2020, Grant Agreement: TBD

SPHERESPHERE aims at providing an integrated operating system framework for cutting-edge heterogeneous multi-core embedded platforms. Main focus is on: the analysis of applications’ task temporal behaviour, support for fast and predictable communication mechanisms between multiple computing nodes, enforcing security features to guarantee data integrity and nodes confidentiality. Applicability is tested on a pioneering autonomous driving system for the automotive domain.

PRIN (ITA, Grant Agreement: TBD

Concluded projects

The project will develop a software framework to support computation- and memory-intensive industrial workloads, on top of next-generation low-power embedded many-cores. The adoption such platform will reduce power consumption by an order of magnitude, whe compared to current, general-purpose-based systems.

The advent of next-generation many-core embedded platforms has the chance of intercepting this converging need for predictable high-performance, allowing HPC and EC applications to be executed on efficient and powerful heterogeneous architectures integrating general-purpose processors with many-core computing fabrics. P-SOCRATES will tackle this important challenge by merging leading research groups from the HPC and EC communities.

The declared goal of project HERCULES is to obtain an order-of-magnitude improvement in the energy efficiency and cost of time-critical embedded systems based on heterogeneous multi-core platforms across several sectors and application domains.